# Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients

arXiv: https://arxiv.org/abs/2605.16593 · HTML: https://mlsanity.com/paper/2605.16593/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 114 references that are in the map.
- First posted on arXiv 15 May 2026.

## Builds on (most cited first, 9 of 9)

- [Confounding-Robust Policy Improvement](https://mlsanity.com/paper/1805.08593/) (2018)
- [Learning from a Biased Sample](https://mlsanity.com/paper/2209.01754/) (2022)
- [Efficient and Sharp Off-Policy Learning under Unobserved Confounding](https://mlsanity.com/paper/2502.13022/) (2025)
- [Constrained Classification and Policy Learning](https://mlsanity.com/paper/2106.12886/) (2021)
- [Off-Policy Evaluation with Out-of-Sample Guarantees](https://mlsanity.com/paper/2301.08649/) (2023)
- [Minimax-Regret Sample Selection in Randomized Experiments](https://mlsanity.com/paper/2403.01386/) (2024)
- [Policy Learning with Confidence](https://mlsanity.com/paper/2502.10653/) (2025)
- [Instrumental Variable methods to target Hypothetical Estimands with longitudinal repeated measures data: Application to the STEP 1 trial](https://mlsanity.com/paper/2407.02902/) (2024)
- [Bias-Reduced Estimation of Finite Mixtures: An Application to Latent Group Structures in Panel Data](https://mlsanity.com/paper/2601.20197/) (2026)

## Abstract (arXiv)

Decision-makers frequently must choose a single action from a finite set of alternatives -- for example, physicians selecting a treatment, investors choosing a portfolio risk level, or judges determining sentences. To improve outcomes, policymakers often issue policy rules or guidelines to inform such choices. In this paper, I show how to generally derive policy rules from observational data in a multi-action framework under relatively weak assumptions about the underlying structure of the heterogeneous sampled population. Conditional average treatment effects (CATEs) are consistently estimated via a weighted K-means algorithm, assuming the outcome model is correctly specified within each homogeneous subgroup. Feasible policy rules are then implemented via a standard decision tree, allowing for both perfect and imperfect adherence to treatment. The methodology is applied to treatment options for Hepatitis C (HCV) among patients co-infected with human immunodeficiency virus (HIV), a setting in which no uniform guideline exists for modern pharmaceutical therapies. The results identify a subgroup of patients with approximately an 80% probability of spontaneous HCV clearance without treatment. Estimation results also show that reallocating treatments among treated individuals could have reduced total treatment costs by CAN3.6-4.9 million while still increasing aggregate health benefits relative to the status quo. These findings demonstrate that the proposed approach can generate improved, data-driven treatment guidelines for the management of HIV/HCV co-infected patients.